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Compressed Sensing (CS) is a groundbreaking paradigm in image acquisition, challenging the constraints of the Nyquist-Shannon sampling theorem. This enables high-quality image reconstruction using a minimal number of measurements. Neural Networks' potent feature induction capabilities enable advanced data-driven CS methods to achieve high-fidelity image reconstruction. However, achieving satisfactory reconstruction performance, particularly in terms of perceptual quality, remains challenging at extremely low sampling rates. To tackle this challenge, we introduce a novel two-stage image CS framework based on latent diffusion, named LD-CSNet. In the first stage, we utilize an autoencoder pre-trained on a large dataset to represent natural images as low-dimensional latent vectors, establishing prior knowledge distinct from sparsity and effectively reducing the dimensionality of the solution space. In the second stage, we employ a conditional diffusion model for maximum likelihood estimates in the latent space. This is supported by a measurement embedding module designed to encode measurements, making them suitable for a denoising network. This guides the generation process in reconstructing low-dimensional latent vectors. Finally, the image is reconstructed using a pre-trained decoder. Experimental results across multiple public datasets demonstrate LD-CSNet's superior perceptual quality and robustness to noise. It maintains fidelity and visual quality at lower sampling rates. Research findings suggest the promising application of diffusion models in image CS. Future research can focus on developing more appropriate models for the first stage.
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Objectives: Rheumatoid Arthritis (RA) can accelerate atherosclerosis (AS) plaque formation. High prevalence of AS has been demonstrated in early-stage RA patients. Therefore, there is an urgent need to investigate what mechanisms and key molecules accelerate AS in RA to improve the management of RA. Methods: We retrieved gene expression data for RA (GSE45291) and atherosclerosis (GSE28829) from Gene Expression Omnibus (GEO). Seventeen key genes were identified, and the top one candidate hub gene was SLAM family member 8 (SLAMF8). To investigate the role of SLAMF8 in AS and RA, U937 cells were differentiated into macrophages using Phorbol 12-myristate 13-acetate (PMA) and further transformed into foam cells by oxidized low-density lipoprotein (ox-LDL) treatment and siRNA was manipulated to knock down SLAMF8. Flow Cytometry was employed to assess cell state. The mRNA and protein expressions of the genes were investigated using western blot and RT-qPCR. Results: SLAMF8 was screened as a key gene by bioinformatic methods. Compared to Mφ, SLAMF8, TLR4 and inflammatory cytokines, tumor necrosis factor α (TNF-α), and Interleukin 6 (IL-6) were noticeably expressed in foam cells. Knockdown of SLAMF8 could remarkably curtail TLR4, TNF-α, and IL-6 protein levels. Antagonizing SLAMF8 could attenuate inflammatory factors and apoptosis of foam cells by inhibiting the TLR4 pathway, thus mitigating the severity of AS in RA. Conclusions: Our work demonstrated that SLAMF8 promoted AS in patients with RA by inducing inflammation and apoptosis of foam cells via TLR4 signaling. Therefore, SLAMF8 could be a possible therapeutic spot for AS in RA patients.
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Stomata are distributed in nearly all major groups of land plants, with the only exception being liverworts. Instead of having stomata on sporophytes, many complex thalloid liverworts possess air pores in their gametophytes. At present, whether stomata in land plants are derived from a common origin remains under debate.1,2,3 In Arabidopsis thaliana, a core regulatory module for stomatal development comprises members of the bHLH transcription factor (TF) family, including AtSPCH, AtMUTE, and AtFAMA of subfamily Ia and AtSCRM1/2 of subfamily IIIb. Specifically, AtSPCH, AtMUTE, and AtFAMA each successively form heterodimers with AtSCRM1/2, which in turn regulate the entry, division, and differentiation of stomatal lineages.4,5,6,7 In the moss Physcomitrium patens, two SMF (SPCH, MUTE and FAMA) orthologs have been characterized, one of which is functionally conserved in regulating stomatal development.8,9 We here provide experimental evidence that orthologous bHLH TFs in the liverwort Marchantia polymorpha affect air pore spacing as well as the development of the epidermis and gametangiophores. We found that the bHLH Ia and IIIb heterodimeric module is highly conserved in plants. Genetic complementation experiments showed that liverwort SCRM and SMF genes weakly restored a stomata phenotype in atscrm1, atmute, and atfama mutant backgrounds in A. thaliana. In addition, homologs of stomatal development regulators FLP and MYB88 also exist in liverworts and weakly rescued the stomatal phenotype of atflp/myb88 double mutant. These results provide evidence not only for a common origin of all stomata in extant plants but also for relatively simple stomata in the ancestral plant.
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Proteínas de Arabidopsis , Arabidopsis , Hepatófitas , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Fatores de Transcrição Hélice-Alça-Hélice Básicos/metabolismo , Hepatófitas/genética , Hepatófitas/metabolismo , Estômatos de Plantas/fisiologia , Plantas/metabolismo , Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Regulação da Expressão Gênica de PlantasRESUMO
Despite decades of efforts in genome sequencing and functional characterization, some important protein families remain poorly understood. In this study, we report the classification, evolution, and functions of the largely uncharacterized AIM24 protein family in plants, including the identification of a novel subfamily. We show that two AIM24 subfamilies (AIM24-A and AIM24-B) are commonly distributed in major plant groups. These two subfamilies not only have modest sequence similarities and different gene structures but also are of independent bacterial ancestry. We performed comparative functional investigations on the two AIM24 subfamilies using three model plants: the moss Physcomitrium patens, the liverwort Marchantia polymorpha, and the flowering plant Arabidopsis thaliana. Intriguingly, despite their significant differences in sequence and gene structure, both AIM24 subfamilies are involved in ER stress tolerance and the unfolded protein response (UPR). In addition, transformation of the AIM24-A gene from P. patens into the AIM24-B null mutant of A. thaliana could at least partially rescue ER stress tolerance and the UPR. We also discuss the role of AIM24 genes in plant development and other cellular activities. This study provides a unique example of parallel evolution in molecular functions and can serve as a foundation for further investigation of the AIM24 family in plants.
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Arabidopsis , Plantas , Plantas/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Sequência de Bases , Arabidopsis/genética , Arabidopsis/metabolismoRESUMO
Seed plants have evolved mechanisms that maintain the dormancy of mature seeds until the time is appropriate for germination. Seed germination is a critical step in the plant life cycle, and it is an important trait in relation to agricultural production. The process is precisely regulated by various internal and external factors, and in particular by diverse endogenous hormones. Jasmonates (JAs) are one of the main plant hormones that mediate stress responses, and recent studies have provided evidence of their inhibitory effects on seed germination. In this review, we summarize our current understanding of the molecular mechanisms underlying the regulatory roles of JAs during the seed germination stage. We describe the crosstalk between JA and other phytohormones that influence seed germination, such as abscisic acid and gibberellic acid.
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Germinação , Reguladores de Crescimento de Plantas , Reguladores de Crescimento de Plantas/fisiologia , Germinação/fisiologia , Sementes/fisiologia , Ácido Abscísico , Dormência de Plantas , Regulação da Expressão Gênica de PlantasRESUMO
How horizontal gene transfer (HGT) has contributed to the evolution of animals and plants remains a major puzzle. Despite recent progress, defining the overall scale and pattern of HGT events in land plants has been largely elusive. In this study, we performed systematic analyses for acquired genes in different plant groups and throughout land plant evolution. We found that relatively recent HGT events occurred in charophytes and all major land plant groups, but their frequency declined rapidly in seed plants. Two major episodes of HGT events occurred in land plant evolution, corresponding to the early evolution of streptophytes and the origin of land plants, respectively. Importantly, a vast majority of the genes acquired in the two episodes have been retained in descendant groups, affecting numerous activities and processes of land plants. We analyzed some of the acquired genes involved in stress responses, ion and metabolite transport, growth and development, and specialized metabolism, and further assessed the cumulative effects of HGT in land plants.
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Embriófitas , Transferência Genética Horizontal , Animais , Embriófitas/genética , Transferência Genética Horizontal/genética , SementesRESUMO
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
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Inteligência Artificial , Verduras , AlgoritmosRESUMO
The unique morphology of grass stomata enables rapid responses to environmental changes. Deciphering the basis for these responses is critical for improving food security. We have developed a planta platform of single-nucleus RNA-sequencing by combined fluorescence-activated nuclei flow sorting, and used it to identify cell types in mature and developing stomata from 33,098 nuclei of the maize epidermis-enriched tissues. Guard cells (GCs) and subsidiary cells (SCs) displayed differential expression of genes, besides those encoding transporters, involved in the abscisic acid, CO2, Ca2+, starch metabolism, and blue light signaling pathways, implicating coordinated signal integration in speedy stomatal responses, and of genes affecting cell wall plasticity, implying a more sophisticated relationship between GCs and SCs in stomatal development and dumbbell-shaped guard cell formation. The trajectory of stomatal development identified in young tissues, and by comparison to the bulk RNA-seq data of the MUTE defective mutant in stomatal development, confirmed known features, and shed light on key participants in stomatal development. Our study provides a valuable, comprehensive, and fundamental foundation for further insights into grass stomatal function.
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Estômatos de Plantas , Zea mays , Humanos , Folhas de Planta/metabolismo , Estômatos de Plantas/metabolismo , Poaceae/genética , Transcriptoma/genética , Zea mays/genéticaRESUMO
Aegilops tauschii, the wild progenitor of wheat D-genome and a valuable germplasm for wheat improvement, has a wide natural distribution from eastern Turkey to China. However, the phylogenetic relationship and dispersion history of Ae. tauschii in China has not been scientifically clarified. In this study, we genotyped 208 accessions (with 104 in China) using ddRAD sequencing and 55K SNP array, and classified the population into six sublineages. Three possible spreading routes or events were identified, resulting in specific distribution patterns, with four sublineages found in Xinjiang, one in Qinghai, two in Shaanxi and one in Henan. We also established the correlation of SNP-based, karyotype-based and spike-morphology-based techniques to demonstrate the internal classification of Ae. tauschii, and developed consensus dataset with 1245 putative accessions by merging data previously published. Our analysis suggested that eight inter-lineage accessions could be assigned to the putative Lineage 3 and these accessions would help to conserve the genetic diversity of the species. By developing the consensus phylogenetic relationships of Ae. tauschii, our work validated the hypothesis on the dispersal history of Ae. tauschii in China, and contributed to the efficient and comprehensive germplasm-mining of the species.
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Aegilops , China , Genótipo , Filogenia , Poaceae/genética , Triticum/genéticaRESUMO
Instrumentation for flow cytometry and sorting is designed around the assumption that samples are single-cell suspensions. However, with few exceptions, higher plants comprise complex multicellular tissues and organs, in which the individual cells are held together by shared cell walls. Single-cell suspensions can be obtained through digestion of the cells walls and release of the so-called protoplasts (plants without their cell wall). Here we describe best practices for protoplast preparation, and for analysis through flow cytometry and cell sorting. Finally, the numerous downstream applications involving sorted protoplasts are discussed.
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Protoplastos , Separação Celular , Citometria de Fluxo , SuspensõesRESUMO
The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.
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This study primarily investigates image sensing at low sampling rates with convolutional neural networks (CNN) for specific applications. To improve the image acquisition efficiency in energy-limited systems, this study, inspired by compressed sensing, proposes a fully learnable model for task-driven image-compressed sensing (FLCS). The FLCS, based on Deep Convolution Generative Adversarial Networks (DCGAN) and Variational Auto-encoder (VAE), divides the image-compressed sensing model into three learnable parts, i.e., the Sampler, the Solver and the Rebuilder. To be specific, a measurement matrix suitable for a type of image is obtained by training the Sampler. The Solver calculates the image's low-dimensional representation with the measurements. The Rebuilder learns a mapping from the low-dimensional latent space to the image space. All the mentioned could be trained jointly or individually for a range of application scenarios. The pre-trained FLCS reconstructs images with few iterations for task-driven compressed sensing. As indicated from the experimental results, compared with existing approaches, the proposed method could significantly improve the reconstructed images' quality while decreasing the running time. This study is of great significance for the application of image-compressed sensing at low sampling rates.
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Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Redes Neurais de ComputaçãoRESUMO
Paulownias are among the fastest growing trees in the world, but they often suffer tremendous loss of wood production due to infection by Paulownia witches' broom (PaWB) phytoplasmas. In this study, we have sequenced and assembled a high-quality nuclear genome of Paulownia fortunei, a commonly cultivated paulownia species. The assembled genome of P. fortunei is 511.6 Mb in size, with 93.2% of its sequences anchored to 20 pseudo-chromosomes, and it contains 31 985 protein-coding genes. Phylogenomic analyses show that the family Paulowniaceae is sister to a clade composed of Phrymaceae and Orobanchaceae. Higher photosynthetic efficiency is achieved by integrating C3 photosynthesis and the crassulacean acid metabolism pathway, which may contribute to the extremely fast growth habit of paulownia trees. Comparative transcriptome analyses reveal modules related to cambial growth and development, photosynthesis, and defense responses. Additional genome sequencing of PaWB phytoplasma, combined with functional analyses, indicates that the effector PaWB-SAP54 interacts directly with Paulownia PfSPLa, which in turn causes the degradation of PfSPLa by the ubiquitin-mediated pathway and leads to the formation of witches' broom. Taken together, these results provide significant insights into the biology of paulownias and the regulatory mechanism for the formation of PaWB.
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Genoma de Planta , Lamiales/crescimento & desenvolvimento , Lamiales/genética , Árvores/crescimento & desenvolvimento , Evolução Molecular , Agricultura Florestal , Redes Reguladoras de Genes , Lamiales/classificação , Anotação de Sequência Molecular , Fotossíntese/genética , Filogenia , Phytoplasma/genética , Phytoplasma/fisiologia , Doenças das Plantas/microbiologia , Proteínas de Plantas/metabolismo , Árvores/genética , Sequenciamento Completo do GenomaRESUMO
Increasing crop production is necessary to feed the world's expanding population, and crop breeders often utilize genetic variations to improve crop yield and quality. However, the narrow diversity of the wheat D genome seriously restricts its selective breeding. A practical solution is to exploit the genomic variations of Aegilops tauschii via introgression. Here, we established a rapid introgression platform for transferring the overall genetic variations of A. tauschii to elite wheats, thereby enriching the wheat germplasm pool. To accelerate the process, we assembled four new reference genomes, resequenced 278 accessions of A. tauschii and constructed the variation landscape of this wheat progenitor species. Genome comparisons highlighted diverse functional genes or novel haplotypes with potential applications in wheat improvement. We constructed the core germplasm of A. tauschii, including 85 accessions covering more than 99% of the species' overall genetic variations. This was crossed with elite wheat cultivars to generate an A. tauschii-wheat synthetic octoploid wheat (A-WSOW) pool. Laboratory and field analysis with two examples of the introgression lines confirmed its great potential for wheat breeding. Our high-quality reference genomes, genomic variation landscape of A. tauschii and the A-WSOW pool provide valuable resources to facilitate gene discovery and breeding in wheat.
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Aegilops/genética , Introgressão Genética , Genoma de Planta , Melhoramento Vegetal/métodos , Triticum/genética , Elementos de DNA Transponíveis , Genética Populacional , Família Multigênica/genética , Filogenia , Proteínas de Plantas/genética , Poliploidia , Locos de Características Quantitativas , Sementes/genética , Sementes/crescimento & desenvolvimentoRESUMO
Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images. Recognizing that a residual image usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose a deep shearlet residual learning network (DSRLN) to estimate the residual images based on the shearlet transform. The proposed network is trained in the shearlet transform-domain which provides an optimal sparse approximation of the cartoon-like image. Specifically, to address the large statistical variation among the shearlet coefficients, a dual-path training strategy and a data weighting technique are proposed. Extensive evaluations on general natural image datasets as well as remote sensing image datasets show that the proposed DSRLN scheme achieves close results in PSNR to the state-of-the-art deep learning methods, using much less network parameters.
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Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.
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Arabidopsis/citologia , Citometria de Fluxo , Células Vegetais , Protoplastos/citologia , Arabidopsis/metabolismo , Protoplastos/metabolismoRESUMO
In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.
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Briófitas , Micorrizas , Briófitas/genética , Fungos/genética , Genoma Fúngico , Genômica , SimbioseRESUMO
An amendment to this paper has been published and can be accessed via a link at the top of the paper.